Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations

Jianfeng Chi; Ujjwal Karn; Hongyuan Zhan; Eric Smith; Javier Rando; Yiming Zhang; Kate Plawiak; Zacharie Delpierre Coudert; Kartikeya Upasani; Mahesh Pasupuleti

arXiv · 2411.10414

Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations

Jianfeng Chi·Ujjwal Karn·Hongyuan Zhan·Eric Smith·Javier Rando·Yiming Zhang·Kate Plawiak·Zacharie Delpierre Coudert·Kartikeya Upasani·Mahesh Pasupuleti

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Abstract

We introduce Llama Guard 3 Vision, a multimodal LLM-based safeguard for human-AI conversations that involves image understanding: it can be used to safeguard content for both multimodal LLM inputs (prompt classification) and outputs (response classification). Unlike the previous text-only Llama Guard versions (Inan et al., 2023; Llama Team, 2024b,a), it is specifically designed to support image reasoning use cases and is optimized to detect harmful multimodal (text and image) prompts and text responses to these prompts. Llama Guard 3 Vision is fine-tuned on Llama 3.2-Vision and demonstrates strong performance on the internal benchmarks using the MLCommons taxonomy. We also test its robustness against adversarial attacks. We believe that Llama Guard 3 Vision serves as a good starting point to build more capable and robust content moderation tools for human-AI conversation with multimodal capabilities.

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Jianfeng Chi, Ujjwal Karn, Hongyuan Zhan, Eric Smith, Javier Rando, Yiming Zhang, Kate Plawiak, Zacharie Delpierre Coudert, Kartikeya Upasani, Mahesh Pasupuleti. 2024-11-15. Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations. https://arxiv.org/abs/2411.10414

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